#!/usr/bin/env python # CREATED: 2013-10-06 22:31:29 by Dawen Liang # unit tests for librosa.decompose # Disable cache import os try: os.environ.pop("LIBROSA_CACHE_DIR") except: pass import numpy as np import scipy.sparse import librosa import sklearn.decomposition import pytest from test_core import srand def test_default_decompose(): X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]]) (W, H) = librosa.decompose.decompose(X, random_state=0) assert np.allclose(X, W.dot(H), rtol=1e-2, atol=1e-2) def test_given_decompose(): D = sklearn.decomposition.NMF(random_state=0) X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]]) (W, H) = librosa.decompose.decompose(X, transformer=D) assert np.allclose(X, W.dot(H), rtol=1e-2, atol=1e-2) def test_decompose_fit(): srand() D = sklearn.decomposition.NMF(random_state=0) X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]]) # Do a first fit (W, H) = librosa.decompose.decompose(X, transformer=D, fit=True) # Make random data and decompose with the same basis X = np.asarray(np.random.randn(*X.shape) ** 2) (W2, H2) = librosa.decompose.decompose(X, transformer=D, fit=False) # Make sure the basis hasn't changed assert np.allclose(W, W2) @pytest.mark.xfail(raises=librosa.ParameterError) def test_decompose_multi_sort(): librosa.decompose.decompose(np.zeros((3,3,3)), sort=True) @pytest.mark.filterwarnings("ignore:Maximum number of iterations") def test_decompose_multi(): srand() X = np.random.random_sample(size=(2, 20, 100)) # Fit with multichannel data components, activations = librosa.decompose.decompose(X, n_components=20, random_state=0) # Reshape the data Xflat = np.vstack([X[0], X[1]]) c_flat, a_flat = librosa.decompose.decompose(Xflat, n_components=20, random_state=0) assert np.allclose(c_flat[:X.shape[1]], components[0]) assert np.allclose(c_flat[X.shape[1]:], components[1]) assert np.allclose(activations, a_flat) @pytest.mark.xfail(raises=librosa.ParameterError) def test_decompose_fit_false(): X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]]) (W, H) = librosa.decompose.decompose(X, fit=False) def test_sorted_decompose(): X = np.array([[1, 2, 3, 4, 5, 6], [1, 1, 1.2, 1, 0.8, 1]]) (W, H) = librosa.decompose.decompose(X, sort=True, random_state=0) assert np.allclose(X, W.dot(H), rtol=1e-2, atol=1e-2) @pytest.fixture def y22050(): y, _ = librosa.load(os.path.join("tests", "data", "test1_22050.wav")) return y @pytest.fixture def D22050(y22050): return librosa.stft(y22050) @pytest.fixture def S22050(D22050): return np.abs(D22050) @pytest.mark.parametrize("window", [31, (5, 5)]) @pytest.mark.parametrize("power", [1, 2, 10]) @pytest.mark.parametrize("mask", [False, True]) @pytest.mark.parametrize("margin", [1.0, 3.0, (1.0, 1.0), (9.0, 10.0)]) def test_real_hpss(S22050, window, power, mask, margin): H, P = librosa.decompose.hpss( S22050, kernel_size=window, power=power, mask=mask, margin=margin ) if margin == 1.0 or margin == (1.0, 1.0): if mask: assert np.allclose(H + P, np.ones_like(S22050)) else: assert np.allclose(H + P, S22050) else: if mask: assert np.all(H + P <= np.ones_like(S22050)) else: assert np.all(H + P <= S22050) @pytest.mark.xfail(raises=librosa.ParameterError) def test_hpss_margin_error(S22050): H, P = librosa.decompose.hpss(S22050, margin=0.9) def test_complex_hpss(D22050): H, P = librosa.decompose.hpss(D22050) assert np.allclose(H + P, D22050) def test_nn_filter_mean(): srand() X = np.random.randn(10, 100) # Build a recurrence matrix, just for testing purposes rec = librosa.segment.recurrence_matrix(X) X_filtered = librosa.decompose.nn_filter(X) # Normalize the recurrence matrix so dotting computes an average rec = librosa.util.normalize(rec.astype(float), axis=0, norm=1) assert np.allclose(X_filtered, X.dot(rec)) def test_nn_filter_mean_rec(): srand() X = np.random.randn(10, 100) # Build a recurrence matrix, just for testing purposes rec = librosa.segment.recurrence_matrix(X) # Knock out the first three rows of links rec[:, :3] = False X_filtered = librosa.decompose.nn_filter(X, rec=rec) for i in range(3): assert np.allclose(X_filtered[:, i], X[:, i]) # Normalize the recurrence matrix rec = librosa.util.normalize(rec.astype(float), axis=0, norm=1) assert np.allclose(X_filtered[:, 3:], (X.dot(rec))[:, 3:]) def test_nn_filter_mean_rec_sparse(): srand() X = np.random.randn(10, 100) # Build a recurrence matrix, just for testing purposes rec = librosa.segment.recurrence_matrix(X, sparse=True) X_filtered = librosa.decompose.nn_filter(X, rec=rec) # Normalize the recurrence matrix rec = librosa.util.normalize(rec.toarray().astype(float), axis=0, norm=1) assert np.allclose(X_filtered, (X.dot(rec))) @pytest.fixture(scope="module") def s_multi(): y, sr = librosa.load(os.path.join("tests", "data", "test1_44100.wav"), sr=None, mono=False) return np.abs(librosa.stft(y)) @pytest.mark.parametrize('useR,sparse', [(False, False), (True, False), (True, True)]) def test_nn_filter_multi(s_multi, useR, sparse): R = librosa.segment.recurrence_matrix(s_multi, mode='affinity', sparse=sparse) if useR: R_multi = R else: R_multi = None s_filt = librosa.decompose.nn_filter(s_multi, rec=R_multi, mode='affinity', sparse=sparse) # Always use the same recurrence matrix for comparison s_filt0 = librosa.decompose.nn_filter(s_multi[0], rec=R) s_filt1 = librosa.decompose.nn_filter(s_multi[1], rec=R) assert np.allclose(s_filt[0], s_filt0) assert np.allclose(s_filt[1], s_filt1) assert not np.allclose(s_filt0, s_filt1) def test_nn_filter_avg(): srand() X = np.random.randn(10, 100) # Build a recurrence matrix, just for testing purposes rec = librosa.segment.recurrence_matrix(X, mode="affinity") X_filtered = librosa.decompose.nn_filter(X, rec=rec, aggregate=np.average) # Normalize the recurrence matrix so dotting computes an average rec = librosa.util.normalize(rec, axis=0, norm=1) assert np.allclose(X_filtered, X.dot(rec)) @pytest.mark.xfail(raises=librosa.ParameterError) @pytest.mark.parametrize( "x,y", [(10, 10), (100, 20), (20, 100), (100, 101), (101, 101)] ) @pytest.mark.parametrize("sparse", [False, True]) @pytest.mark.parametrize("data", [np.zeros((10, 100))]) def test_nn_filter_badselfsim(data, x, y, sparse): srand() # Build a recurrence matrix, just for testing purposes rec = np.random.randn(x, y) if sparse: rec = scipy.sparse.csr_matrix(rec) librosa.decompose.nn_filter(data, rec=rec)